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name: tf-agents description: "Select, configure, debug, and adapt Acme TensorFlow/Sonnet agents, networks, savers, Launchpad examples, and TF losses." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Acme TensorFlow Agents

Use this sub-skill when the user asks about Acme acme.agents.tf algorithms, Sonnet/TensorFlow network construction, TensorFlow learner debugging, Launchpad distributed TF examples, TF losses, or TF saving/snapshotting.

Route By Task

• Choose or compare TF algorithm families: read references/agent-catalog.md, then run python scripts/select_tf_agent.py --help or pass task signals to the selector.
• Build or adapt Sonnet policy, critic, observation, recurrent, Atari, OpenSpiel/legal-action, or offline learner networks: read references/network-and-saver-workflows.md.
• Add TF checkpoints, snapshots, variable syncing, or learner state handling: read references/network-and-saver-workflows.md.
• Debug TF optional dependencies, @tf.function graph behavior, Sonnet shape/dtype errors, Launchpad flags, OpenSpiel legal-action masks, or Gym/Atari setup: read references/troubleshooting.md.

Boundaries

• This sub-skill owns TensorFlow/Sonnet algorithms under acme.agents.tf, acme.tf.networks, acme.tf.losses, acme.tf.savers, acme.tf.utils, and acme.tf.variable_utils.
• Route JAX algorithms, Haiku/Optax/RLax patterns, and JAX-only examples to the sibling jax-agents sub-skill.
• Route environment loops, wrappers, observers, loggers, and generic Acme agent-loop structure to core-workflows unless the question is TF-specific.
• Route Reverb table, adder, dataset iterator, and offline data mechanics to replay-and-data; this sub-skill only names the TF agent parameters that consume those objects.

Quick Defaults

• Use the dm-acme[tf] optional dependency set for real TF execution; it pins TensorFlow 2.8.0, TensorFlow Probability 0.15.0, TensorFlow Datasets 4.6.0, Reverb 0.7.2, Launchpad 0.5.2, plus Sonnet and TRFL.
• TF agents usually accept Sonnet modules for policy_network, critic_network, observation_network, or a discrete/recurrent network; distributed variants accept factories and expose a Launchpad build(name=...) method.
• For Control Suite distributed runs, keep three layers separate: environment_factory, network_factory, then lp.launch(program_builder.build(), launch_type=...) with --lp_launch_type=local_mt or local_mp for local debugging.
• For learner debugging, temporarily remove or bypass the learner _step() @tf.function decorator, inspect tensors in eager mode, then restore graph mode after the issue is fixed.

Bundled Helper

• scripts/select_tf_agent.py maps task signals such as --action-space discrete --setting distributed --needs recurrent to candidate TF agents and example families without importing TensorFlow.